The invention relates to the field of video analysis, such as the comparison and finding of correspondence and similarity between video sequences.
Matching of video sequences is an important problem in video analysis and computer vision. It is a cornerstone component in many commercial applications, including video search, content based retrieval, video authentication and copyright detection, to mention a few.
One good example of prior art work in video sequence matching is J. Sivic and A. Zisserman, “Video Google: a text retrieval approach to object matching in video”, Ninth IEEE International Conference on Computer Vision (ICCV'03)—Volume 2, 2003, iccv, p. 1470. These authors describe an approach to object and scene retrieval which searches and localizes all the occurrences of a user outlined object in a video.
One problem with such prior art methods, however, is that because such prior art video analysis methods tended to approach video as a collection of images, these approaches were both computationally intensive and prone to high error rates. In particular, such earlier “single-frame” image analysis methods had little ability to distinguish between, for example, an image of an apple (fruit) in the context of images of fruits, and the image of an the same apple (same image, now used as a logo) in the context of a series of images of computers.
Thus, there is a need for less computationally intensive, higher reliability video analysis methods that do a better job of interpreting individual video images within their overall video context.